I'm a noob but it's not what I expect - periods change what is extracted in inconsistent ways.
e.g.
"I love Berlin." -> "Berlin."
"I love Berlin ." -> "Berlin"
"George Washington loves Berlin." -> "George Washington"
"George Washington loves Berlin ." -> ["George Washington", "Berlin"]
In that simple example you posted, they already did the tokenization manually, as it's pretty trivial. But yes, in many cases, you have preprocessors that do the tokenization. In some libraries, you actually have a class/object_type for tokens, but it's pretty common to just preprocess and take every space as a token separator.
In some contexts and cases, it's possible to see tokens like "social_network", where multiple words are considered a single token.
In that first tutorial, they also mention they have a tokenizer if you need it: "In some use cases, you might not have your text already tokenized. For this case, we added a simple tokenizer using the lightweight segtok library."
So for your example you would simply run the tokenizer first, then the named entity recognition.
EDIT: apparently you can do this directly: "sentence = Sentence('The grass is green.', use_tokenizer=True)"